SoroushVahidi/scidocs huggingface dataset:
Functional near infrared spectroscopy (fNIRS): an emerging neuroimaging technology with important applications for the study of brain disorders.
(2007)

Functional near-infrared spectroscopy (fNIRS) is an emerging functional neuroimaging technology offering a relatively non-invasive, safe, portable, and low-cost method of indirect and direct monitoring of brain activity. Most exciting is its potential to allow more ecologically valid investigations that can translate laboratory work into more realistic everyday settings and clinical environments. Our aim is to acquaint clinicians and researchers with the unique and beneficial characteristics of fNIRS by reviewing its relative merits and limitations vis-à-vis other brain-imaging technologies such as functional magnetic resonance imaging (fMRI). We review cross-validation work between fMRI and fNIRS, and discuss possible reservations about its deployment in clinical research and practice. Finally, because there is no comprehensive review of applications of fNIRS to brain disorders, we also review findings from the few studies utilizing fNIRS to investigate neurocognitive processes associated with neurological (Alzheimer's disease, Parkinson's disease, epilepsy, traumatic brain injury) and psychiatric disorders (schizophrenia, mood disorders, anxiety disorders).
Dose the Use of Big Data Analytics Guarantee a High Firm Performance? An Empirical Study Bases on the Dynamic Capabilities Theory
(2018)

For the sake of enriching relevant research field, this study focuses on two main questions: (1) What is the effect of DBA usage on firm performance (including market performance and operational performance)? and (2) What factors that drive organizations to use big data analytics (BDA) are key drivers, based on dynamic capabilities theory? And we also identified the moderating effect of environmental dynamism between BDA use and firm performance. Furthermore, we introduce the need pull/technology push framework to identify and theorize paths via which factors influence the use of BDA. Purely from the need pull/technology push theory perspective, we propose that perceived usefulness of BDA should belong to need pull factor. Thus, we select perceived usefulness and satisfaction level with existing IT technology as need pull factors, BDA use of competitors and technology compatibility as technology push factors for big data analytics (BDA) use, and examines if these factors have a positive effect on big data analytics (BDA).
Performance Comparison between Five NoSQL Databases
(2016)

Recently NoSQL databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of NoSQL databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five NoSQL clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool – YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to NoSQL developers and users.
Cathodic protection by zinc sacrificial anodes: impact on marine sediment metallic contamination.
(2009)

Cathodic protection by sacrificial zinc anodes is often applied to prevent immerged metallic structures from corrosion. But this technique induces the zinc anodes dissolution, which can induce marine sediments and seawater contamination. A large scale experiment, in natural seawater, was conducted during 12 months, in order to evaluate the potential environmental impact of this continuous zinc dissolution, and of some necessary cleaning operations of the anodes surfaces. The heavy metal (Cr, Cu, Pb and Zn) concentration in water and sediment samples was monitored. A sequential extraction procedure was applied on sediment samples to differentiate the zinc mobile fractions from the residual one. A significant increase of zinc concentration was observed in water as well as in the surface sediments under the specific operating conditions. Sediments then become a secondary pollution source, as the sorbed labile zinc can be remobilized to seawater.
Larger-Context Language Modelling with Recurrent Neural Network
(2016)

In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained largercontext language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.
Title Parental Expectations and Children ' s Academic Performance in Sociocultural Context
(2010)

In this paper, we review research on parental expectations and their effects on student achievement within and across diverse racial and ethnic groups. Our review suggests that the level of parental expectations varies by racial/ethnic group, and that students' previous academic performance is a less influential determinant of parental expectations among racial/ethnic minority parents than among European American parents. To explain this pattern, we identify three processes associated with race/ethnicity that moderate the relation between students' previous performance and parental expectations. Our review also indicates that the relation of parental expectations to concurrent or future student achievement outcomes is weaker for racial/ethnic minority families than for European American families. We describe four mediating processes by which high parental expectations may influence children's academic trajectories and show how these processes are associated with racial/ethnic status. The article concludes with a discussion of educational implications as well as suggestions for future research.
Frequency Enhancement in Miller Divider with Injection-Locking Portrait
(2017)

In this paper, we present a methodology to enhance the operating range of a Miller frequency divider. Conventionally Miller frequency dividers are visualized as mixers. We present how injection locking portrait of Miller dividers helps us to understand the dynamics of Miller frequency dividers. Further, we discuss how to enhance the operating range while optimizing the divider between power dissipation and operating range. The Miller divider with the proposed methodology has been designed in a standard CMOS 65 nm (low leakage) process. Post layout simulation results show that Miller divider with optimization techniques presented in this paper can operate over the frequency range of 13 GHz for 0dBm input power at center frequency of 61.5 GHz.
Does Attendance Matter ? An Examination of Student Attitudes , Participation , Performance and Attendance
(2006)

Non attendance of lectures and tutorials appears to be a growing trend. The literature suggests many possible reasons including students’ changing lifestyle, attitudes, teaching and technology. This paper looks at the reasons for non attendance of students in the Faculty of Commerce at the University of Wollongong and identifies relationships between attendance, participation and performance. The results indicate that there are valid reasons for non attendance that are both in the control of learners and teachers. There are also clear benefits for students to be gained in attendance; however, changes in the way we learn, teach, assess and use technology are recommended if we wish to reverse the trend. This journal article is available in Journal of University Teaching & Learning Practice: http://ro.uow.edu.au/jutlp/vol3/iss2/3 Jour na l o f Un ive rs i t y Teach ing and Lear n ing Pr ac t i ce Does Attendance Matter? An Examination of Student Attitudes, Participation, Performance and Attendance
Applied machine vision of plants: a review with implications for field deployment in automated farming operations
(2010)

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.
SmartPaste: Learning to Adapt Source Code
(2017)

Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this work, we introduce SmartPaste, a first task that requires to use such information. The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code. As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way. Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.
Analysis and reduction of quadrature errors in the material point method ( MPM )
(2008)

The material point method (MPM) has demonstrated itself as a computationally effective particle method for solving solid mechanics problems involving large deformations and/or fragmentation of structures, which are sometimes problematic for finite element methods (FEMs). However, similar to most methods that employ mixed Lagrangian (particle) and Eulerian strategies, analysis of the method is not straightforward. The lack of an analysis framework for MPM, as is found in FEMs, makes it challenging to explain anomalies found in its employment and makes it difficult to propose methodology improvements with predictable outcomes. In this paper we present an analysis of the quadrature errors found in the computation of (material) internal force in MPM and use this analysis to direct proposed improvements. In particular, we demonstrate that lack of regularity in the grid functions used for representing the solution to the equations of motion can hamper spatial convergence of the method. We propose the use of a quadratic B-spline basis for representing solutions on the grid, and we demonstrate computationally and explain theoretically why such a small change can have a significant impact on the reduction in the internal force quadrature error (and corresponding ‘grid crossing error’) often experienced when using MPM. Copyright q 2008 John Wiley & Sons, Ltd.
Handbook of Natural Language Processing
(2010)

Handbook of Natural Language Processing and Machine Translation · Learning Natural Language Processing With Python and NLTK p.1 Tokenizing words and By far, the most popular toolkit or API to do natural language processing... Nitin Indurkhya, Fred J. Damerau, Handbook of Natural Language Processing, Second Edition. Chapman & Hall/CRC Machine Learning & Pattern Recognition. Arabic Natural Language Processing, Machine Translation Handbook of Natural Language Processing and Machine Translation, 164-175, 2011. 2011.
A construction of cryptography system based on quantum neural network
(2016)

Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs).The purpose of this paper is to introduce an application of QNNs in construction of cryptography system in which two networks exchange their outputs (in qubits) and the key to be synchronized between two communicating parties. This system is based on multilayer qubit QNNs trained with back-propagation algorithm.
A Multi-View Fusion Neural Network for Answer Selection
(2018)

Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications. Previous neural network-based methods consider several different aspects of information through calculating attentions. These different kinds of attentions are always simply summed up and can be seen as a “single view”, causing severe information loss. To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a “view” of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation. In this fusion RNN method, a filter gate collects important information of input and directly adds it to the output, which borrows the idea of residual networks. Experimental results on the WikiQA and SemEval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods.
Calming effects of deep touch pressure in patients with autistic disorder, college students, and animals.
(1992)

ABSTRACT Many people with autistic disorder have problems with oversensitivity to both touch and sound. The author (an autistic person) developed a device that delivers deep touch pressure to help her learn to tolerate touching and to reduce anxiety and nervousness. The "squeeze machine" applies lateral, inwardly directed pressure to both lateral aspects of a person's entire body, by compressing the user between two foam-padded panels. Clinical observations and several studies suggest that deep touch pressure is therapeutically beneficial for both children with autistic disorder and probably children with attention-deficit hyperactivity disorder. Only minor and occasional adverse effects have been noted. Data are reported that show a similar calming effect in nonreferred college students. A review of the animal literature reveals that animals have similar calming reactions, and also suggests possible additional physiological effects of deep touch pressure. At present, there are increasing anecdotal reports of the clinical value of the squeeze machine, including suggestions that it can be used to reduce required doses of psychostimulant medications. More clinical studies are needed to evaluate the potential role of this seemingly beneficial form of "physiological" stimulation.
"Oops, I Did It Again" - Security of One-Time Signatures Under Two-Message Attacks
(2016)

One-time signatures (OTS) are called one-time, because the accompanying security reductions only guarantee security under single-message attacks. However, this does not imply that efficient attacks are possible under two-message attacks. Especially in the context of hash-based OTS (which are basic building blocks of recent standardization proposals) this leads to the question if accidental reuse of a one-time key pair leads to immediate loss of security or to graceful degradation. In this work we analyze the security of the most prominent hash-based OTS, Lamport’s scheme, its optimized variant, and WOTS, under different kinds of two-message attacks. Interestingly, it turns out that the schemes are still secure under two message attacks, asymptotically. However, this does not imply anything for typical parameters. Our results show that for Lamport’s scheme, security only slowly degrades in the relevant attack scenarios and typical parameters are still somewhat secure, even in case of a two-message attack. As we move on to optimized Lamport and its generalization WOTS, security degrades faster and faster, and typical parameters do not provide any reasonable level of security under two-message attacks.
Multi-granularity Generator for Temporal Action Proposal
(2018)

Temporal action proposal generation is an important task, aiming to localize the video segments containing human actions in an untrimmed video. In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information. First, we propose to use a bilinear matching model to exploit the rich local information within the video sequence. Afterwards, two components, namely segment proposal generator (SPG) and frame actionness generator (FAG), are combined to perform the task of temporal action proposal at two distinct granularities. SPG considers the whole video in the form of feature pyramid and generates segment proposals from one coarse perspective, while FAG carries out a finer actionness evaluation for each video frame. Our proposed MGG can be trained in an end-to-end fashion. Through temporally adjusting the segment proposals with fine-grained information based on frame actionness, MGG achieves the superior performance over state-of-the-art methods on the public THUMOS-14 and ActivityNet-1.3 datasets. Moreover, we employ existing action classifiers to perform the classification of the proposals generated by MGG, leading to significant improvements compared against the competing methods for the video detection task.
The human amygdala and the emotional evaluation of sensory stimuli
(2003)

A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.
Economic dispatch for a microgrid considering renewable energy cost functions
(2012)

Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.
All-digital PLL and transmitter for mobile phones
(2005)

We present the first all-digital PLL and polar transmitter for mobile phones. They are part of a single-chip GSM/EDGE transceiver SoC fabricated in a 90 nm digital CMOS process. The circuits are architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrateable with a digital baseband and application processor. To achieve this, we exploit the new paradigm of a deep-submicron CMOS process environment by leveraging on the fast switching times of MOS transistors, the fine lithography and the precise device matching, while avoiding problems related to the limited voltage headroom. The transmitter architecture is fully digital and utilizes the wideband direct frequency modulation capability of the all-digital PLL. The amplitude modulation is realized digitally by regulating the number of active NMOS transistor switches in accordance with the instantaneous amplitude. The conventional RF frequency synthesizer architecture, based on a voltage-controlled oscillator and phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter. The transmitter performs GMSK modulation with less than 0.5/spl deg/ rms phase error, -165 dBc/Hz phase noise at 20 MHz offset, and 10 /spl mu/s settling time. The 8-PSK EDGE spectral mask is met with 1.2% EVM. The transmitter occupies 1.5 mm/sup 2/ and consumes 42 mA at 1.2 V supply while producing 6 dBm RF output power.
A Design Procedure for All-Digital Phase-Locked Loops Based on a Charge-Pump Phase-Locked-Loop Analogy
(2007)

In this brief, a systematic design procedure for a second-order all-digital phase-locked loop (PLL) is proposed. The design procedure is based on the analogy between a type-II second-order analog PLL and an all-digital PLL. The all-digital PLL design inherits the frequency response and stability characteristics of the analog prototype PLL